Virtualization and Cloud executives share their predictions for 2017. Read them in this 9th annual VMblog.com series exclusive.
Contributed by Dr. William L. Bain, CEO and founder, and Chris Villinger, vice president, business development and marketing, ScaleOut Software
Operational Intelligence Goes Mainstream with New Focus on Live Systems
business intelligence alone can no longer keep pace in
today's data-driven market. Companies also need real-time analysis of
their data streams to capture perishable business opportunities and make
mission-critical decisions in industries like manufacturing, e-commerce
and finance. In 2017, we foresee the following shifts as companies
discover competitive advantages through gaining real-time insight into
2017, the need for "operational" intelligence to capture highly dynamic
business opportunities will shift the focus of big data from the data
warehouse to live systems.
the last several years, the big data revolution has popularized Hadoop
and other technologies that capture business intelligence in the data
warehouse. While there continues to be a place for business intelligence
to perform "after-the-fact" analysis of historic data and inform
strategic decision making, businesses also need to analyze live streams
of fast-changing data in order to generate immediate feedback that
boosts ROI. We call this
"operational intelligence," and it picks up where business
intelligence leaves off. The need for operational intelligence to
maximize competitiveness will drive its adoption in a
wide range of industries, including e-commerce, finance,
manufacturing, patient-monitoring, transportation, and utilities. In
2017, we expect to see widespread integration of this exciting
capability into live systems.
in-memory computing will enter the mainstream as the enabling technology
for adding operational intelligence to live systems, and it will
supplant legacy streaming technologies.
In 2017, the adoption of in-memory computing technologies, such as in-memory data grids (IMDGs), will provide the enabling technology to capture perishable opportunities and make mission-critical decisions on live data. Driven
by the need for real-time analytics, the IMDG market alone - currently
estimated at $600 million - will exceed $1 billion by 2018, according
Unlike big data
technologies, such as Spark, created for the data warehouse and legacy
streaming technologies, in-memory computing enables the straightforward
modeling and tracking of a live system by analyzing and correlating
persistent data with live fast-changing data in real time, and
it provides immediate feedback to that system for automated decision
making. Gartner has recently elevated the term "digital twin"
in its recent Top 10 strategic technology trends for 2017 to
describe the shift in focus from data streams to the data sources which
produce those streams. In-memory computing technology enables
applications to easily create and manage digital representations of
real-world devices, such as Industrial Internet of Things (IIoT) sensors and actuators, and this enables real-time introspection for operational intelligence.
In-memory computing techniques will leverage the power of machine learning to enhance the value of operational intelligence.
The year 2017
will see an accelerated adoption of scenarios that integrate machine
learning with the power of in-memory computing, especially in e-commerce
systems and the Internet of Things (IoT).
E-commerce applications benefit by offering highly personalized
experiences created by tracking and analyzing dynamic shopping
behavior. IoT applications,
such as those associated with windmills and solar arrays, benefit by
delivering predictive feedback based on rapidly emerging patterns. In
both of these applications, machine learning techniques can dramatically
deepen the introspection and enhance operational intelligence.
practical only on supercomputers, machine learning techniques have
evolved to become increasingly available on standard, commodity
hardware. This enables IMDGs to apply them to the analysis of fast
changing data and specifically to dynamic digital models of live
systems. The ability of IMDGs to perform iterative computation in
real-time and at extreme scale enables machine learning techniques to be
easily integrated into stream processing which provides operational
About the Authors
Dr. William L. Bain, CEO and founder, ScaleOut Software
Software was founded in 2003 by Dr. William L. Bain. Bill has a Ph.D.
(1978) in electrical engineering/parallel computing from Rice
University, and he has worked at Bell Labs research, Intel, and
Microsoft. Bill founded and ran three start-up companies prior to
joining Microsoft. In the most recent company (Valence Research), he
developed a distributed Web load-balancing software solution that was
acquired by Microsoft and is now called Network Load Balancing within
the Windows Server operating system. Dr. Bain holds several patents in
computer architecture and distributed computing. As a member
of the screening committee for the Seattle-based Alliance of Angels,
Dr. Bain is actively involved in entrepreneurship and the angel
Chris Villinger, vice president, business development and marketing, ScaleOut Software
has over 18 years' experience at global, high-tech multinationals,
including Microsoft and Philips, in software marketing, marketing
technology, content management, global digital marketing and commercial
sales networks, systems integration consulting, and business planning
and forecasting. Chris has a multicultural US and European background
with fluency in four major languages. He holds a BSEE degree from Tulane
University and a Bilingual International MBA from the IESE Business
School of the University of Navarra in Barcelona Spain.